CoPiT: Cognitive Pivot Translation for Digraphic Low-Resource Mongolian in the Traditional Script
arXiv:2607.05849v1 Announce Type: new Abstract: Low-resource languages remain challenging for machine translation, and Mongolian is a representative case. As a digraphic language, Mongolian is written in both Cyrillic and Traditional scripts, which exhibit a severe imbalance in data availability. While the Cyrillic script is relatively well-resourced, the Traditional script remains extremely data-scarce and orthographically ambiguous, leading to substantial performance degradation in direct translation. We propose CoPiT, a cognitively motivated pivot-based translation pipeline that exploits th
The continuous drive for more inclusive and effective AI models, especially for under-resourced languages, combined with advancements in cognitive AI techniques, makes this development timely.
Improving machine translation for low-resource languages like Mongolian in their traditional script reduces information asymmetry and enhances digital accessibility, potentially fostering economic and cultural exchange.
Machine translation capabilities for digraphic, low-resource languages, specifically Traditional Mongolian, are significantly enhanced, enabling broader digital integration and data utilization of previously inaccessible text.
- · Mongolian-speaking communities
- · AI language model developers
- · Multinational organizations operating in Mongolia
- · Linguistics researchers
- · Exclusive reliance on major language AI models
- · Translation services specializing in higher-resource languages
Increased digital content creation and access in Traditional Mongolian script.
Potential for greater economic integration and digital services for Mongolian communities previously limited by language barriers.
Improved geopolitical intelligence and cultural understanding through effective processing of previously inaccessible data in diverse scripts.
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